Abstract: In an apparatus for recognizing a lane partition line, a pre-branch section setting unit sets a pre-branch section of a road on which an own vehicle is traveling. The pre-branch section extends a section length from a start line that is positioned closer to the own vehicle than a branch location by an offset distance in a traveling direction of the own vehicle. A determination unit determines whether or not a geographical location of the own vehicle is within the pre-branch section. If it is determined that the geographical location of the own vehicle is within the pre-branch section, a recognition unit suppresses recognition of the lane partition line on an diverging road side of the road on which the own vehicle is traveling. The diverging road is another road branching off from the road on which the vehicle is traveling at the branch location.

Abstract: In a lane boundary line recognition device, a calculation section calculates a degree of uncertainty which affects a correct recognition of white lines on a roadway of a vehicle. A learning section updates a learning value of the degree of uncertainty. A recognition suppression section suppresses execution of a recognition process of recognizing white lines on the roadway when the updated learning value is more than a threshold value. An environment change judgment section judges whether or not a road environment has changed. A learning resetting section resets the learning value of the degree of uncertainty to a previous learning value when the detection result of the environment change judgment section indicates an occurrence of change of the road environment.

Abstract: An apparatus for determining the presence or absence of a turnoff from a roadway. In the apparatus, a white-line candidate extractor applies image processing to an image of surroundings of a subject vehicle acquired by a vehicle-mounted camera to extract white-line candidates in the roadway. A white-line likelihood calculator calculates, for each of the white-line candidates extracted by the white-line candidate extractor, a likelihood of the white-line candidate. A white-line likelihood calculator calculates, for each of the white-line candidates extracted by the white-line candidate extractor, a likelihood of the white-line candidate. A turnoff determiner calculates a likelihood for one of a plurality of features of the white line selected by the white-line selector, and determines the presence or absence of a turnoff from the roadway based on the likelihood calculated by the turnoff determiner.

Abstract: In a pitch angle estimation apparatus, an image acquirer sequentially acquires images in a forward or rearward direction of a vehicle. Each image is referred to as a frame. A movement amount calculator extracts a feature point from each of the images acquired by the image acquirer and calculates an amount of movement of the feature point in a vertical direction of the image. A pitch angle calculator calculates a pitch angle of the vehicle based on the amount of movement of the feature point. An output unit outputs the pitch angle calculated by the pitch angle calculator. A low frequency component calculator calculates a low frequency component of the pitch angle calculated by the pitch angle calculator. An output determiner determines whether to output the pitch angle based on the magnitude of the low frequency component calculated by the low frequency component calculator.

Abstract: In a lane line recognition apparatus, an abnormality determiner is configured to determine whether or not a width of a travel lane defined by left-side and right-side lane lines is abnormal, and a lane-line recognizer is configured to, when the width of the travel lane is not abnormal, recognize both of the left-side and right-side lane lines in a both-side line recognition mode, and when the width of the travel lane is abnormal, recognize one of the lane lines in a one-side line recognition mode. In the one-side line recognition mode, the lane-line recognizer is configured to, for each of the left-side and right-side lane lines, calculate two or more parameters of the lane line, and then integrate recognition results for the respective two or more parameters, and based on the integrated recognition results of the left-side and right-side lane lines, select one of the lane lines to be recognized.

Abstract: A lane boundary line recognition device detects lane boundary line candidates of a roadway from images captured by an in-vehicle camera, judges that the lane boundary line candidate is a lane boundary line of a branch road, and calculates a curvature of the lane boundary line candidate, and recognizes the lane boundary line based on the calculated curvature. The device removes the lane boundary line candidate, which has been judged as the lane boundary line of the branch road, is removed from a group of the lane boundary line candidates, and calculates the curvature of the lane boundary line candidate based on an estimated rate of change of the curvature. The device uses a past curvature calculated predetermined-number of images before when the lane boundary line candidate is the lane boundary line of the branch road, and resets the estimated rate of change of the curvature to zero.

Abstract: A driving support apparatus installed in a host vehicle calculates an overlap ratio relating to a target object positioned ahead of the host vehicle within the same traffic lane, as the ratio of the width of the target object to its lateral distance from a lane marker line of the traffic lane, and also detects the motion condition of the object (i.e., stationary, moving towards, or in the same direction as the host vehicle, or moving laterally with respect to the forward direction of the host vehicle). An amount of compensation for retarding or advancing the commencement of a collision avoidance operation by the driving support apparatus is determined based on the overlap ratio and/or the motion condition of the target object.

Abstract: An apparatus for recognizing a lane line. In the apparatus, when a three-dimensional object lies in the same lane as a subject vehicle and a distance between the three-dimensional object and the subject vehicle is small in an image acquired by an image capture unit, a masking area setter sets a masking area that is partially truncated at or near a lower end of the three-dimensional object in the image. A degree-of-belief calculator is configured to, for each of the edge points extracted by the edge-point extractor, calculate a degree of belief that the edge point is on the lane line. The degree of belief when the edge point is in the masking area is set less than the degree of belief when the edge point is outside the masking area. A lane-line recognizer is configured to recognize the lane line based on the degrees of belief calculated for the edge points.

Abstract: An intersection recognition device installed in a vehicle includes an image acquisition unit that acquires images around the moving vehicle. The device includes an extraction unit that extracts, from the images acquired by the image acquisition unit, a type and a position of at least one traffic marking existing around the vehicle. The device includes a determination unit that determines whether an intersection exists based on the type of the at least one traffic marking extracted by the extraction unit and a relative position relationship between the at least one traffic marking and the vehicle.

Abstract: A traveling path estimation apparatus includes a calculation section that calculates a traveling marking line marking a lane of a road, on which a vehicle runs, based on a front image acquired by a camera mounted in the vehicle, an estimation section that estimates road parameters including a curvature and a curvature change rate of the lane, the estimation section estimating the road parameters at current time based on the traveling marking line calculated by the calculation section and the road parameters previously estimated, a determination section that determines departure of the vehicle from a curve of the lane, and a reset section that, when the determination section determines the departure of the vehicle, resets at least the curvature change rate included in the road parameters previously estimated by the estimation section.

Abstract: A driving support apparatus installed in a host vehicle calculates an overlap ratio relating to a target object positioned ahead of the host vehicle within the same traffic lane, as the ratio of the width of the target object to its lateral distance from a lane marker line of the traffic lane, and also detects the motion condition of the object (i.e., stationary, moving towards, or in the same direction as the host vehicle, or moving laterally with respect to the forward direction of the host vehicle). An amount of compensation for retarding or advancing the commencement of a collision avoidance operation by the driving support apparatus is determined based on the overlap ratio and/or the motion condition of the target object.

Abstract: In a lane boundary line recognition device, an extraction unit extracts lane boundary line candidates from image acquired by an in-vehicle camera. A position estimation unit estimates a position of each lane boundary line based on drive lane information containing a number of drive lanes on a roadway and a width of each drive lane when (a) and (b) are satisfied, (a) when an own vehicle drives on an own vehicle lane specified by the drive lane specifying unit, and (b) when the lane boundary line candidate corresponds to lane boundary lines of the own vehicle lane. A likelihood calculation unit increases a likelihood of the lane boundary line candidate when a distance between a position of the lane boundary line candidate and an estimated position of the lane boundary line candidate obtained by the drive lane boundary line position estimation unit is within a predetermined range.

Abstract: A complex marking determining device includes a brightness state calculating portion configured to calculate a lane marking representative value indicative of a brightness state of a plurality of pixels that make up a lane marking display region, and calculate a road surface representative value indicative of a brightness state of a plurality of pixels that make up the road surface display region; and a complex marking determining portion configured to determine that the lane marking is a complex marking at least when a degree of deviation between the lane marking representative value and the road surface representative value is equal to or greater than a first threshold value.

Abstract: In an image processing apparatus, an image data acquisition unit acquires image data including R-pixels which are red pixels, holding red luminance values, B-pixels which are blue pixels, holding blue luminance values, G-pixels which are green pixels, holding green luminance values, and C-pixels which are clear pixels, holding luminance values in a wavelength range including wavelength ranges corresponding to red, blue and green. In the apparatus, a determination unit determines whether or not an inequality given by Valc?Valx<? is satisfied, where Valx is a luminance value of a pixel of interest that is one of the R-, B-, and G-pixels in the image data, Valc is a luminance value of the C-pixel or pixels closest to the pixel of interest, and ? is a constant, and if the inequality is satisfied, a correction unit corrects the luminance value Valx to be less than the luminance value Valc.

Abstract: In an image processing apparatus, an image acquiring unit acquires a first image and a second image that form stereoscopic images. A first sub-image extracting unit extracts first sub-images from the first image. A second sub-image extracting unit extracts second sub-images from the second image. A matching unit matches each pair of the first and second sub-images to determine a degree of similarity therebetween. A similar sub-image setting unit sets the second sub-image having a highest degree of similarity to the first sub-image to be a similar sub-image to the first sub-image. A brightness comparing unit compares in brightness each pair of the first and second sub-images. The matching unit is configured to, if a result of comparison made by the brightness comparing unit between a pair of the first and second sub-images is out of a predetermined brightness range, exclude such a pair of the first and second sub-images.

Abstract: A lane partition line recognition apparatus for recognizing left-side and right-side lane partition lines of a traveling lane of a roadway in which a vehicle carrying the apparatus is traveling based on a forward image captured by a vehicle-mounted camera. The apparatus includes an offset detector configured to detect an offset amount indicative of a positional relationship between each of the left-side and right-side recognized lane partition lines and the vehicle and a predictor configured to, if only one of the left and right lane partition lines of the traveling lane of the subject vehicle is recognized, stochastically predict a position of the unrecognized one of the left and right lane partition lines based on the offset amount detected by the offset detector.

Abstract: A lane boundary line recognition apparatus acquires an image. Based on edge points extracted from the image, a lane boundary line candidate is extracted, and a lane boundary line probability thereof is calculated. A lane boundary line candidate of which the lane boundary line probability exceeds a threshold is recognized. As the threshold, a first threshold, larger than a second threshold in normal mode, is set when a first condition is met and also a second condition is met. The first condition is that a single lane boundary line is recognized on a roadside side of a lane in which the own vehicle is traveling, and a composite lane boundary line is recognized on a side opposite the roadside side. The second condition is that a state in which a lane boundary line cannot be recognized on at least the opposite side continues for a predetermined amount of time.

Abstract: A lane boundary line recognition apparatus, mounted to an own vehicle, images a road surface ahead of the own vehicle and acquires an image. Edge points are extracted from the image. A lane boundary line candidate is extracted based the edge points. A lane boundary line probability of the lane boundary line candidate is calculated. A lane boundary line candidate of which the lane boundary line probability exceeds a predetermined threshold is recognized as a lane boundary line. A solid object is recognized in an image. A lane boundary line being hidden by the solid object is detected. When a lane boundary line being hidden is detected, the lane boundary line probability is suppressed in at least a part of an area outside of the hidden lane boundary line, compared to that when the lane boundary line being hidden is not detected.

Abstract: A monitoring apparatus is provided. When a determination process of determining whether or not the part of an object located in a search range is an airborne substance, a rangefinder measures a distance to the object located in the search range for each of a plurality of unit areas forming the search range, a variation measure calculator calculates a variance of the distances (individual distances) measured for the respective unit areas, a variable threshold setter variably sets a variation measure threshold based on the individual distances, and a determiner determines that at least part of the object is an airborne substance if the calculated variance exceeds the variation measure threshold.

Abstract: A travel lane marking recognition apparatus includes a recognizing unit that is mounted to a vehicle. The recognizing unit uses a recognition model to recognize a lane marking that demarcates a lane on a road, based on an image of an area ahead of the vehicle captured by an on-board camera. The recognition model is configured by a plurality of recognition models that includes at least two recognition models among a first-order model that takes into consideration a straight line, a second-order model that takes into consideration a steady curve, and a third-order model that takes into consideration a clothoid curve. The recognizing unit is configured to recognize the lane marking by integrating recognition results acquired using the plurality of recognition models based on a state of the vehicle.